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检索条件"主题词=Expectation-Maximisation algorithm"

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Skills Transfer Across Dissimilar Robots by Learning Context-Dependent Rewards

Skills Transfer Across Dissimilar Robots by Learning Context...

引用

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Malekzadeh, Milad S. Bruno, Danilo Calinon, Sylvain Nanayakkara, Thrishantha Caldwell, Darwin G. IIT Dept Adv Robot Via Morego 30 I-16163 Genoa Italy Univ London King Coll Div Engn London WC1E 7HU England

ISBN: (纸本)9781467363587

Robot programming by demonstration encompasses a wide range of learning strategies, from simple mimicking of the demonstrator's actions to the higher level extraction of the underlying intent. By focusing on this last form, we study the problem of extracting the reward function explaining the demonstrations from a set of candidate reward functions, and using this information for self-refinement of the skill. This definition of the problem has links with inverse reinforcement learning problems in which the robot autonomously extracts an optimal reward function that defines the goal of the task. By relying on Gaussian mixture models, the proposed approach learns how the different candidate reward functions are combined, and in which contexts or phases of the task they are relevant for explaining the user's demonstrations. The extracted reward profile is then exploited to improve the skill with a self-refinement approach based on expectation-maximization, allowing the imitator to reach a skill level that goes beyond the demonstrations. The approach can be used to reproduce a skill in different ways or to transfer tasks across robots of different structures. The proposed approach is tested in simulation with a new type of continuum robot (STIFF-FLOP), using kinesthetic demonstrations from a Barrett WAM manipulator.

关键词： Gaussian processes expectation-maximisation algorithm learning (artificial intelligence) manipulators robot programming

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Online Quantum Mixture Regression for Trajectory Learning by Demonstration

Online Quantum Mixture Regression for Trajectory Learning by...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Korkinof, Dimitrios Demiris, Yiannis Univ London Imperial Coll Sci Technol & Med Dept Elect & Elect Engn London SW7 2BT England

ISBN: (纸本)9781467363587

In this work, we present the online Quantum Mixture Model (oQMM), which combines the merits of quantum mechanics and stochastic optimization. More specifically it allows for quantum effects on the mixture states, which in turn become a superposition of conventional mixture states. We propose an efficient stochastic online learning algorithm based on the online expectation Maximization (EM), as well as a generation and decay scheme for model components. Our method is suitable for complex robotic applications, where data is abundant or where we wish to iteratively refine our model and conduct predictions during the course of learning. With a synthetic example, we show that the algorithm can achieve higher numerical stability. We also empirically demonstrate the efficacy of our method in well-known regression benchmark datasets. Under a trajectory Learning by Demonstration setting we employ a multi-shot learning application in joint angle space, where we observe higher quality of learning and reproduction. We compare against popular and well-established methods, widely adopted across the robotics community.

关键词： expectation-maximisation algorithm iterative methods learning (artificial intelligence) numerical stability regression analysis robots stochastic processes

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On-line Handwritten Mathematical Expression Recognition Method based on Statistical and Semantic Analysis

On-line Handwritten Mathematical Expression Recognition Meth...

引用

11th IAPR International Workshop on Document Analysis Systems (DAS)

作者： Hu, Yang Peng, Liangrui Tang, Yejun Tsinghua Univ Dept Elect Engn Tsinghua Natl Lab Informat Sci & Technol Beijing 100084 Peoples R China

ISBN: (纸本)9781479932436

Recognition of handwritten mathematical expressions (HMEs) has become a cutting edge research topic recently, as there are increasingly needs for pen-inputting applications. In this paper, we presented a novel framework to analyse HME layout and semantic information. This framework includes three steps, namely symbol segmentation, symbol recognition and semantic relationship analysis. For symbol segmentation, a decomposition on strokes is operated, then dynamic programming is adopted to find the paths corresponding to the best segmentation manner and reduce the stroke searching complexity. For symbol recognition, spatial geometry and directional element features are classified by a Gaussian Mixture Model learnt through expectation-Maximization algorithm. At last, in the semantic relationship analysis module, a ternary tree is utilized to to store the ranked symbols through calculating the operator priorities. The motivation for our work comes from the apparent difference in writing styles across western and Chinese populations. Our results are reasonable and show promise on the private dataset.

关键词： Handwritten Mathematical Expression Online Recognition Statistical and Semantic Analysis on-line identification Semantics expectation-maximisation algorithm expression recognition Stroke Recognition (Psychology) dynamic programming segmentation Segmentation

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Supervised Topic Regression via Experts

Supervised Topic Regression via Experts

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International Joint Conference on Neural Networks (IJCNN)

作者： Lin, Song Guo, Ping Xin, Xin Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China

ISBN: (纸本)9781479914845

This paper focuses on the research issue of supervised topic models. Traditionally, an unsupervised topic model is typically supervised by incorporating a supervised linear operator. Although this kind of methods has successfully achieved supervised topic representations, as well as tractable computational complexity, the main limitation lies in that it assumes the topic data is linearly distributed. Therefore, when the practical data does not follow the linear property, the model cannot perform well. To solve this problem, a non-linear supervised topic model is proposed in this paper. Specifically, the mixture of experts (ME), as a kind of "divide and conquer", is utilized to deal with the regression of non-linear of topic data. We integrate the mixture of experts and unsupervised latent Dirichlet allocation (LDA) in a Bayesian manner. The proposed model can also reduce overfitting problem of ME with inputting high-dimensional data. An elegant learning algorithm is derived based on variational expectation maximization algorithm. Experimental results show that the proposed model has better predictive performances compared with usupervised topic models, and some state-of-the-art supervised topic regression models (including sLDA model and MedLDA model), on two textual datasets and one image dataset.

关键词： Professional personnel Regression (Psychology) expectation-maximisation algorithm Dataset

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Applying Fuzzy EM algorithm with a Fast Convergence to GMMs

Applying Fuzzy EM Algorithm with a Fast Convergence to GMMs

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2010 IEEE World Congress on Computational Intelligence

作者： Ju, Zhaojie Liu, Honghai Univ Portsmouth Sch Creat Technol Intelligent Syst & Robot Grp Portsmouth PO1 2UP Hants England

ISBN: (纸本)9781424469208

Inspired from the mechanism of Fuzzy C-means (FCMs) which introduces a degree of fuzziness on the dissimilarity function based on distances, a fuzzy expectation Maximization (EM) algorithm for Gaussian Mixture Models (GMMs) is proposed in this paper. In the fuzzy EM algorithm, the dissimilarity function is defined as the multiplicative inverse of probability density function. Different from FCMs, the defined dissimilarity function is based on the exponential function of the distance. The fuzzy EM algorithm is compared with normal EM algorithm in terms of fitting degree and convergence speed. The experimental results in modeling random data and various characters demonstrate the ability of the proposed algorithm in reducing the computational cost of GMMs.

关键词： Gaussian mixture model Gaussian processes convergence convergence speed dissimilarity function expectation-maximisation algorithm exponential function fast convergence fitting degree fuzziness degree fuzzy EM algorithm fuzzy c-means fuzzy expectation maximization fuzzy set theory multiplicative inverse normal EM algorithm pattern clustering probability probability density function

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Low-Complexity Leakage-Based Carrier Frequency Offset Estimation Techniques for OFDMA Uplink Systems

Low-Complexity Leakage-Based Carrier Frequency Offset Estima...

引用

IEEE Global Telecommunications Conference (GLOBECOM 2010)

作者： Lee, Kilbom Moon, Sung-Hyun Lee, Inkyu Korea Univ Sch Elect Engn Seoul South Korea

ISBN: (纸本)9781424456383

In this paper, we propose an efficient carrier frequency offset (CFO) estimation technique based on the space alternating generalized expectation-maximization (SAGE) for uplink orthogonal frequency division multiple access (OFDMA) systems. In general, the SAGE method transforms a multidimensional search problem into a sequence of one-dimensional searches, which greatly simplifies the estimation procedure. However, the conventional algorithms based on the SAGE method require a large amount of computations to estimate the CFO due to exhaustive grid search. To reduce the computational burden, we exploit the leakage on the fast Fourier transform (FFT) output of the received signal after the multiple access interference is removed by the SAGE method. Then, this leakage-based approach reduces the complexity of the conventional SAGE algorithm regardless of an employed carrier assignment scheme by avoiding grid search. Simulation results show that our modified SAGE algorithm approaches the Cramer Rao bound at all signal to noise ratio (SNR) region with greatly reduced complexity compared to the conventional SAGE algorithms.

关键词： Cramer Rao bound OFDM modulation OFDMA uplink systems carrier frequency offset estimation estimation theory exhaustive grid search expectation-maximisation algorithm fast Fourier transform fast Fourier transforms frequency division multiple access interfe

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Unsupervised Motion Learning from a Moving Platform

Unsupervised Motion Learning from a Moving Platform

引用

IEEE Intelligent Vehicles Symposium

作者： Romero-Cano, Victor Nieto, Juan I. Agamennoni, Gabriel Univ Sydney Australian Ctr Field Robot Sydney NSW 2006 Australia

ISBN: (纸本)9781479907946;9781479907953

Learning motion patterns in dynamic environments is a key component of any context-aware robotic system, and probabilistic mixture models provide a sound framework for mining these patterns. This paper presents an approach for learning motion models from trajectories provided by the tracking system of a moving platform. We present a learning approach in which a Linear Dynamical System (LDS) is augmented with a discrete hidden variable that has a number of states equal to the number of behaviours in the environment. As a result, a mixture of linear dynamical systems (MLDSs) capable of explaining several motion behaviours is developed. The model is learned by means of the expectation Maximization (EM) algorithm.

关键词： data mining driver information systems expectation-maximisation algorithm unsupervised learning

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Non-binary LDPC decoding using truncated messages in the Walsh-Hadamard domain

Non-binary LDPC decoding using truncated messages in the Wal...

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International Symposium on Information Theory and its Applications (ISITA)

作者： Sayir, Jossy Univ Cambridge Cambridge CB2 1TN England

ISBN: (纸本)9784885522925

The Extended Min-Sum (EMS) algorithm for non-binary low-density parity-check (LDPC) defined over an alphabet of size q operates on truncated messages of length q' to achieve a complexity of the order q'(2). In contrast, Walsh-Hadamard (WH) transform based iterative decoders achieve a complexity of the order q log q, which is much larger for q' << q. In this paper, we demonstrate that considerable savings can be achieved by letting WH based decoders operate on truncated messages as well. We concentrate on the direct WH transform and compute the number of operations required if only q' of the q inputs are non-zero. Our paper does not cover the inverse WH transform and hence further research is needed to construct WH based decoders that can compete with the EMS algorithm on complexity terms.

关键词： Approximation methods Complexity theory Decoding Energy management Parity check codes Transforms Vectors Transform parity check codes DECODING Complexity theory Approximation method Energy management decoders Messages expectation-maximisation algorithm low density parity check codes

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An Iterative Method of Sensor Calibration in Participatory Sensing Network

An Iterative Method of Sensor Calibration in Participatory S...

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IEEE 10th International Conference on Mobile Ad-Hoc and Sensor Systems (MASS)

作者： Xiang, Chaocan Yang, Panlong PLA Univ Sci & Technol Coll Commun Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9780769551043

With widespread usages of smart phones, participatory sensing becomes mainstream, especially for applications requiring pervasive deployments with massive sensors. However, sensors on smart phones are prone to the unknown measurement errors, requiring automatical calibration among uncooperative participants. Current methods need either collaboration or explicit calibration process. However, due to the uncooperative and uncontrollable nature of the participants, these methods fail to calibrate sensor nodes effectively. We investigate sensor calibration in monitoring pollution sources, without explicit calibration process in uncooperative environment. We leverage the opportunity in sensing diversity, where a participant will sense multiple pollution sources when roaming in the area. Further, inspired by EM (expectation Maximization) method, we propose a two-level iterative algorithm to estimate the source presences, source parameters and sensor noise iteratively. Our algorithm can converge to the optimal estimation of sensor noise, where the likelihood of observations is maximized.

关键词： calibration expectation-maximisation algorithm measurement errors monitoring parameter estimation smart phones wireless sensor networks

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Hidden Markov Model Estimation Based on Alpha-EM algorithm: Discrete and Continuous Alpha-HMMs

Hidden Markov Model Estimation Based on Alpha-EM Algorithm: ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Matsuyama, Yasuo Waseda Univ Dept Comp Sci & Engn Tokyo 1698555 Japan

ISBN: (纸本)9781424496365

Fast estimation algorithms for Hidden Markov models (HMMs) for given data are presented. These algorithms start from the alpha-EM algorithm which includes the traditional log-EM as its proper subset. Since existing or traditional HMMs are the outcome of the log-EM, it had been expected that the alpha-HMM would exist. In this paper, it is shown that this foresight is true by using methods of the iteration index shift and likelihood ratio expansion. In each iteration, new update equations utilize one-step past terms which are computed and stored during the previous maximization step. Therefore, iteration speedup directly appears as that of CPU time. Since the new method is theoretically based on the alpha-EM, all of its properties are inherited. There are eight types of alpha-HMMs derived. They are discrete, continuous, semi-continuous and discrete-continuous alpha-HMMs, and both for single and multiple sequences. Using the properties of the alpha-EM algorithm, the speedup property is theoretically analyzed. Experimental results including real world data are given.

关键词： algorithm design and analysis Approximation algorithms Covariance matrix Equations Estimation Hidden Markov models Mathematical model alpha-EM algorithm continuous alpha-HMM discrete alpha-HMM expectation-maximisation algorithm hidden Markov model estimation hidden Markov models iteration index shift likelihood ratio expansion

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